A single ChIP-seq dataset is sufficient for comprehensive analysis of motifs co-occurrence with MCOT package

Abstract

Recognition of composite elements consisting of two transcription factor binding sites gets behind the studies of tissue-, stage- and condition-specific transcription. Genome-wide data on transcription factor binding generated with ChIP-seq method facilitate an identification of composite elements, but the existing bioinformatics tools either require ChIP-seq datasets for both partner transcription factors, or omit composite elements with motifs overlapping. Here we present an universal Motifs Co-Occurrence Tool (MCOT) that retrieves maximum information about overrepresented composite elements from a single ChIP-seq dataset. This includes homo- and heterotypic composite elements of four mutual orientations of motifs, separated with a spacer or overlapping, even if recognition of motifs within composite element requires various stringencies. Analysis of 52 ChIP-seq datasets for 18 human transcription factors confirmed that for over 60% of analyzed datasets and transcription factors predicted co-occurrence of motifs implied experimentally proven protein-protein interaction of respecting transcription factors. Analysis of 164 ChIP-seq datasets for 57 mammalian transcription factors showed that abundance of predicted composite elements with an overlap of motifs compared to those with a spacer more than doubled; and they had 1.5-fold increase of asymmetrical pairs of motifs with one more conservative ‘leading’ motif and another one ‘guided’.

Specification and regulation of vascular tissue identity in the Arabidopsis embryo

“…Indeed, Gbox motifs were found to be enriched in close proximity to auxin response elements (AuxREs) (Weiste and Dröge-Laser, 2014;Ulmasov et al, 1995) and they are overrepresented in auxin-responsive and ARF-binding regions (Berendzen et al, 2012;Cherenkov et al, 2018). To test the co-occurrence of ARFs and G-class bZIPs motifs, we applied the Motifs Co-Occurrence Tool (MCOT) (Levitsky et al, 2019) to ARF5 and ARF2 peaks taken from genome-wide DAP-seq profiles (O'Malley et al, 2016). We analyzed all possible combinations of AuxREs (ARF2/5 motifs) and Gboxes (GBF3 and bZIP16/68 motifs) with any overlap or spacer lengths below 30 nucleotides, and found that bZIP68 and ARF5 motifs overlap (P-value<5E-40) ( Fig.…”

Integrating Peak Colocalization and Motif Enrichment Analysis for the Discovery of Genome-Wide Regulatory Modules and Transcription Factor Recruitment Rules

“…There are a few main differences between PscanChIP and other methods for the same task. The presence/absence of a motif instance in a region is evaluated with a score, ranging from 0 to 1, instead of a yes/no decision (binding motif present/absent) as for example in recent works (Dergilev et al, 2017;Czipa et al, 2020;Levitsky et al, 2019), which are also focused on the analysis of regions surrounding ChIP-Seq summits. Mean and variance of scores of best motif instances in each of the summit regions are in turn employed by PscanChIP to assess motif enrichment not only with respect to regions flanking peaks (local enrichment), as in similar tools (Zhang et al, 2011;Bailey and MacHanick, 2012), but also with respect to the rest of the genome, providing a more accurate evaluation of their significance.…”

“…An orthogonal approach is to analyze regions resulting from a single ChIP-Seq experiment for enrichment of sequence motifs known to represent sites be bound by other TFs, as for example in (Wang et al, 2012;Levitsky et al, 2019). Candidate TFs thus identified can be likely members of the same regulatory module.…”

Candidate SNP Markers of Atherogenesis Significantly Shifting the Affinity of TATA-Binding Protein for Human Gene Promoters Show Stabilizing Natural Selection as a Sum of Neutral Drift Accelerating Atherogenesis and Directional Natural Selection Slowing It

“…This approach should exclude them from clinical studies, which consequently could become more targeted and less expensive. Although the accuracy of current genome-wide computational predictions remains below the applicability threshold for clinical SNP tests [26], this accuracy increases every year [27][28][29][30][31][32].…”